The human genome has been decoded, but the real work begins now: to discover the function of the genes and their interaction in order to develop novel therapeutic agents drugs to treat diseases. The use of microarrays is one of the main tools in order to do this. The analysis of microarrays, which is an essential component for state of the art research, is the focus of our proposal. Our approach will be multidisciplinary with the purpose of serving the molecular biology area, but including tools from biology, mathematics, statistics, electrical engineering, and computer science. Microarray experiments are very expensive and usually involve sacrifice of animals. Consequently, an essential component of our research focus will be to develop experimental design tools as well as innovative mathematical models that will minimize the number of experiments. An innovative mathematical model allows us to narrow the research and allows a better study of its properties. This allows generalized models, which are more useful for the applications. This is the kind of work we have done in our generalization of Boolean models. We are currently studying much more complex models. In summary, our work in models will focus on 1) formulating the models, 2) studying their properties, theoretically or with the use the computers, 3) interaction with biological reality, and 4) refinements of the model to better fit the application. Another important component of our research will focus on the development of error correcting capacity for microarrays in order to solve the problem of experimental noise. An important point of our project is our collaboration with the projects of Dr. Sandra Pena (conditioned taste aversion in rats) and Dr. Garcia-Arraras (regeneration in sea cucumbers). In this collaboration they will provide us with their data, and we will use our tools to analyze the data. We will also help in the design of their experiments in order to determine the optimal number of replications. Dr. Pena and Dr. Garcia will help us develop better data-processing/modeling techniques by testing them in their projects as representatives of a larger class of biological experiments.